performance of microfinance institutions in north east...

XVI Annual Conference Proceedings January, 2015

ISBN no. 978-81-923211-7-2 http://www.internationalconference.in/XVI_AIC/INDEX.HTM Page 414

Performance of Microfinance Institutions in North East India: Data

Envelopment Analysis based efficiency analysis

Shibu Das Assistant Professor Department of Commerce Lachit Barphookan Commerce Academy, Nagaon-782001 (Research Scholar, Deptt of Commerce, Assam University)

Abstract

Micro Finance Institutions (MFIs) emerged with unparallel scope to poor people who did not

have access to commercial banks. It involves the provision of micro-credit, savings and other

services to the poor that are excluded by the commercial banks for collateral and other

reasons. It is an empirical study and analytical in nature and the study will be conducted on

secondary data. According to the Mix Market data base ten MFIs are providing services and

all are taken for the study. The outcome of the data analysis revealed sufficient evidence to

establish an efficiency among the branch offices. Accuracy of the efficiency is depend upon

the accuracy of the reported by the North East MFIs. The study will try to identify some of

the hidden facts/ figures relating to the efficiency of the MFIs in Assam. It offers a beneficial

source of information to Asomi, MFIs in Assam. The result of this study would help the MFIs

to better understanding the micro finance and MFIs may try to improve their branch level

efficiency through this research work.

Keywords:- DEA, Efficiency, MFIs, North East India



Introduction

Microcredit is the practice of offering small loans to poor people who are not traditionally

being served by the commercial bank Microcredit was first founded in Bangladesh in the 70s

and 80s by Nobel laureate Mohammad Yunus and the organization he founded, Grameen

Bank. Following the success of Grameen Bank, this financial innovation has not only been

used in Bangladesh alone, but also has been replicating by the most other countries in the

world with some modification based on contextual differences.

A large section of the Indian population is uncovered by formal financial system. In

the year 2011, Indian census report has showed that 102 million households out of a total of

247 million in India are not covered by the banking system and 65 percent of these

households are in rural areas. It is noticeable, however, that between 2001 and 2011 there was

23 percent of increase in the number of household services by banks but this growth was

more prominent in the rural areas1. Microfinance in India evolved in order to fill the gap

created by formal banking sector. Therefore, microfinance is referred to as the alternate

commercial sector targeting the poor2. Microfinance has become a global industry and it is

increasing at a noticeable rate worldwide. It continues to add 13 percent borrowers annually

since 19993. In developing countries Microfinance Institutions (MFIs) emerged with huge

opportunity to the poor people who do not have access to commercial banks. The MFIs face

unique challenges because it must achieve dual objectives- outreach i.e. provide financial

services to the poor and sustainability i.e. cover its cost.4

1 Nair, S. T. (2014, April). Microfinance Regulation in India: A Critical Perspective. Yojana. Vol.58. P 45. 2 Pathak., V. B. (2011). Financial Inclusion and Microfinance. The Indian Financial System. (3rd Edition). New Delhi, India: Pearson. P 734 3 Roy., A (2012, November). Agricultural Finance Vs. Profitability of Microfinance Institutes- A case Study of the MFIs of Assam. Asian pacific Journal of marketing & Management Review Vo. 1 No. 3, Pp 1-2. 4 Das., S. (2014, April). Efficiency measurement of MF is in Assam: A comparative Study In of Asomi Vs. RGVN(NE). International Referred Journal Researchers World. Vol. V. Issue-2(1). P. 13.



Research Limitations:

Accuracy of the efficiency is depend upon the accuracy of the reported by the North East

MFIs.

Significance of the study:

The present study is an attempt to identify some of the hidden facts of NER MFIs.

This study may be helpful to financial institutions in improving their efficiency. Prospective

savers will be able to identify the efficient MFIs of this region.

Objectives of the study

1. To measure the level of technical efficiency of MFIs of the NER; and

Hypotheses:

1. Ho1= There is same level of technical efficiency among MFIs in the NER; and

Methodology:

This Study is an empirical and analytical in nature. It has been conducted on

secondary data. It is based on annual data financial year 2011-12. According to the MIX

Market data base ten MFIs are operating in North East India on 25-04-2014. Based on Data

Envelopment Analysis (DEA), efficiency analysis had been performed to find out the best

practice MFIs among the North East India. Table:1 contains information about MFIs:


ISBN no. 978-81-923211-7-2 http://www.internationalconference.in/XVI_AIC/INDEX.HTM 17

Table: 1 Detail List of NER MFIs

Sl.

No

.

MFIs

Parent

State

Date of

Establishme

nt

Statu

s

Product(s)

Offered

Main Funding

Source(s)

1

ASOMI

Assam 1-1-2001 NBFI Loan Loan,

Shareholders,

Capital

2 RGVN(NE)

Assam 1-1-1996 NBFI Loan Grant, Loan &

Savings

3 GRAMEEN

SAHARA

Assam 1-1-2005 NGO Loan Grant

4 NIGHTANGLE Assam 2-10-1997 NGO Loan Grant

5

BANDHAN

West

Bengal

11-4-2001 NBFI Loan,

Insurance

Loan,

shareholders,

capital

6

KANKLATA

MAHILA URBAN

CO-OPERATIVE

Assam 2009 Co-

Opera

tive

Loan Grant

7 Chanura

Microfinance

Manipur (CMM)

Manipur 2007 NGO Loan Grant

8

Youth Volunteers’

Union

Microfinance

(YMF)

Manipur

1970 NGO Full scale

financial

Services,

Health,

Education,

Loan

Loan,

Shareholders,

Grant

9 Youth Volunteers’

Union Financial

Services (YFS)

Manipur

9-7-1993 NBFI Full scale

financial

Services,

Health,

Education,

Loan,

Shareholders,

Grant



Loan

10 Weaker Section

Development

Society (WSDS)

Manipur 26-7-1989 NGO Health,

Education,

Loan

Loan, Grant

Source: MIX Market data base

Concept of Efficiency:

Production is an act of transforming input into output because the objective of

production is to create value through transformation5. To evaluate the efficiency of firms

relative to the best production, it is necessary to have a quantify standard. That standard can

only be determined by those productive units which share a common technology6.

Input Oriented Measured:

Input oriented measure address the question of ‘by how much can input quantity be

proportionally reduced without changing the output quantities produced’7. It means input(s)

can be reduced without affect the output level.

Output Oriented Measured:

Output Oriented measures TE address the question as to ‘by how much can output

quantity be proportionally expand without altering the input quantities used’8. It means output

can be increased without introducing inputs.

The output oriented measures could be explained focusing a change in output by

using fixed level inputs.

Mathematical Framework:

DEA is a non-parametric method of measuring the efficiency of a Decision Making

Units (DMUs)9, here MFIs are called DMUs. In this technique, both Input oriented measure

and output oriented measure have been used for the comparison of efficiency score10.

5 Ray, C. S. (2004). Data Envelopment Analysis Theory and techniques for Economic and Operations Research. Cambridge University Press, New York. P1. 6 Qayyum, A., & Ahmed, M. (2006). Efficiency and Sustainability of Micro Finance. MMRA Paper No. 1167, posted, November 2008. Retrieved from http:// mpra.up.uni-muenchen.de/11674 on 22-8-2013. Pp. 8 7 Deb, J. (n.d.). Post-Reform bank efficiency in North-East India: a branch level analysis. (Doctoral thesis, North-Eastern Hill University, India). http://shodhganga.inflibnet.ac.in/handle/10603/5308 Retrieved on 20-3-2014 Pp. Chapter V 8 Ibib Chapter V.



DEA is a linear programming methodology to measure the relative performance and

efficiency of multiple DMUs when it involves multiple inputs and outputs.

Advantages of DEA:

Capable of handling multiple inputs and outputs.

Capable of being used with any input- output measurement.

The sources of inefficiency can be analysed and quantified for every evaluated unit.

In DEA, efficiency can be measured by an input oriented process, which focuses on

reducing inputs to produce the same level of output and output oriented process which aims

to maximize output from a given set of input11. The DEA method is based on the common

assumption of Constant Return to Scale (CRS) and the alternative assumption of Variable

Return to Scale (VRS).

In the year 1978, Charnes, Cooper and Rhodes (CCR) provided the following formula to

measure TE under CRS:

Max ∑ ��

Subject to : ∑ �� - ∑ ��

�� ≤ 0

∑ �� = 1 -�� ≤ 0 ; -v ≤ 0.

Where, �� and �� are positive known outputs and inputs of the jth DMU and �� and �� are

the variable weight to be determined by solving above equation problems.

On the other hand, the following model provided by Banker, Charnes and Cooper (1984)

dealt with the VRS version of DEA.

Max ∑ �� - ��

Subject ∑ �� t - ∑ ��

�� - �� ≤ 0

∑ �� = 1

-�� ≤ 0, - �� ≤ 0.

�∗ indicates the return to scale possibilities. An �∗ < 0 implies increasing return to scale and

�∗> 0 implies a decreasing return to scale. Our DEA ranges between 0-100. 9 Ray, C. S. (2004). Data Envelopment Analysis Theory and techniques for Economic and Operations Research. Cambridge University Press, New York. Pp.1 10 Qayyum, A., & Ahmed, M. (2006). Efficiency and Sustainability of Micro Finance. MMRA Paper No. 1167, posted, November 2008. Retrieved from http:// mpra.up.uni-muenchen.de/11674 on 22-8-2013. P. 13. 11 Gebremichael., Z., B. (2013). Efficiency, outreach and sustainability of Ethiopian

microfinance institutions. Doctoral thesis, Andhra University, Andhra Pradesh, India).

http://shodhganga.inflibnet.ac.in/handle/10603/12700 Retrieved on 28-4-2014 P.75



Selection of Inputs and Outputs:

For the study, two inputs and two outputs have been used.

Inputs are : Total No. of employees(X1); and Operating Expenses(X2).

Output is : Gross Loan Portfolio (Y1). (All outstanding principals due for all

outstanding client loans. His includes current, delinquent, and renegotiated loans, but not

loans that have been written off.)

The Empirical Study:

The DEA technical efficiency for 10 MFIs is calculated by assuming both Constant

Return to Scale and Variable Return to Scale technology. While measuring the technical

efficiency of MFIs, both input oriented measured as well as output oriented measures have

been used.

Table: 2 Efficiency Measurements by Model I

(Output Indicator = Gross Loan Portfolio)

Efficiency scores VRS CRS Scale efficiencies Returns-to-

scale

Asomi 0.3521 0.3375 0.9585 increasing

RGVN(NE) 0.5537 0.5493 0.9921 increasing

Grameen Sahara 0.6034 0.5656 0.9374 increasing

Nightangle 0.9164 0.6927 0.7558 increasing

Kanaklata Mahila

Urban Co-Operative 1.0000 0.4339 0.4339 increasing

Bandhan 1.0000 1.0000 1.0000 constant

CMM 0.1541 0.1259 0.8172 increasing

YMF 0.4435 0.3846 0.8673 increasing

YFS 0.6247 0.3190 0.5106 increasing

WSDS 1.0000 0.6821 0.6821 increasing

Mean 0.6648 0.5091 0.7955

Author’s calculation

In Table :2 under them Model I, the result show that one MFI is on the technical

efficient frontier when CRS is assumed, while three MFIs are on the efficient frontier in the



case when VRS is assumed. The MFI that remains technically efficient under both CRS and

VRS assumption is Bandhan. It is fully engaged in microfinance related activities.

In Model I, average output oriented VRS, CRS and Scale efficiency are 66.48%, 50.91% and

79.55% respectively.

Under the output oriented measured the MFIs can increase their loan portfolio by

33.52% with the existing level of input by efficient utilization of the inputs, viz., number of

employees and operating expenses.

Under the output oriented measured 90% of the MFIs are the stage of increasing return to

scale while no one out of ten MFIs included in the study are at decreasing return to scale.

Author’s Calculation

In Table 3 shows the virtual inputs level and output level of the seven MFIs who

could not reach the highest score under the CRS and VRS assumption. Here, virtual inputs

explains that how much total number of employees and operating expenses may be reduced

Table: 3 shows the virtual inputs and output of Model I

DMUs

Total No.

of

Enployees

Reduc

ed

Operating

Expenses

Reduce

d

Gross Loan

Portfolio

Increase

d

Asomi 242.00

0.00%

40,868,057.91 9.45% 906,225,026.50

183.97%

RGVN(NE) 486.42

1.93%

82,256,146.78 0.00%

1,846,461,162.87 80.61%

Grameen

Sahara 73.45

3.36%

10,871,535.23 0.00% 230,591,597.07 65.73%

Nightangle 42.00

0.00% 6,315,758.66

18.74% 124,023,102.96 9.12%

CMM 56.00

0.00% 8,734,419.49

10.83% 178,777,234.94

548.99%

YMF 77.00

0.00%

12,362,410.95

17.23% 260,908,437.85

125.49%

YFS 21.00

0.00% 2,687,767.41

50.20% 41,891,904.99 60.07%



and virtual output means that how much gross loan portfolio may increased without

introducing the additional inputs.

In case of Asomi MFIs could have reduced 9.45% of operating expenses with

increasing 183.97% of Gross loan portfolio. GRVN(NE) MFIs could have reduced 1.93% of

Total No. of Enployees with increasing 80.61% of Gross loan portfolio. Grameen Sahara

MFIs could have reduced 3.36% of Total No. of Enployees with increasing 65.73% of

Gross loan portfolio. Nightangle MFIs could have reduced 18.74% of operating expenses

with increasing 9.12% of Gross loan portfolio. CMM MFIs could have reduced 10.83% of

operating expenses with increasing 548.99% of Gross loan portfolio. YMF MFIs could have

reduced 17.23% of operating expenses with increasing 125.49% of Gross loan portfolio.

YFS MFIs could have reduced 50.20%of operating expenses with increasing 60.07% of

Gross loan portfolio

Correlation Analysis:

This section examines the possible determinants of efficiency of MFIs in North East

India. Here, I suggest different variables that can clarify the efficiency of MFIs.

These variables can be divided into different groups based on age, basic

characteristics, financial management and performance. I used correlation in this section.

First variable considered is the age of the MFI. It is hypothesize become older may

perform better than others.

The second category relates to the features of MFIs in terms of size i.e. total value of

assets (TA). Hypothesize that larger firms may perform better than smaller size.

The last variable that represents the financial management of MFIs is Debt-Equity

ratio. It is imagined that higher debt-equity ratio reduces firm efficiency.

Table: 4 Correlation coefficients between the different Technical efficiency

measured and variables

Variables CRS/TE VRS/PTE SE

Age 0.18972387 0.173984063 -0.050905

Assets 0.705166915 0.383186498 0.3752283

Debt-Equity Ratio 0.219105352 0.120857629 0.1859938

Author’s calculations

In Table: 2 calculated the correlation coefficients between different efficiency

measures and the variables defined above. The results show that the age, value of total assets



and debt/equity ratio are positively correlated with all efficiency measures except age under

scale efficiency assumption.

The result from the correlation coefficient came to conclude that age and size of MFIs

are important in determining the technical efficiency of MFIs.

Conclusion

This paper tried to investigate the efficiency level of ten MFIs in North East india and

determinants of level of technical efficiency. For the efficiency analysis we used non-

parametric Data Envelopment Analysis and considered input oriented and output oriented

methods by assuming both CRS and VRS assumption.

While conducting DEA analysis for the year 2011-12, it has been found that out of 10 there

are one microfinance institution is Bandhan that is on efficiency frontier under constant

return to scale assumption and three microfinance institutions Kanaklata Mahila Urban

Co-Operative, Bandhan and WSDS that are on efficiency frontier under variable return to

scale assumption.

The inefficiency of MFIs in NER is mainly of technical nature. In order to increase

the efficiency of the MFIs, there is the need of accelerating managerial skill and improving

policy. Then it will meet the dual objectives of outreach and financial sustainability.



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